Exploration of COVID-19 tracking data from multiple resources
Wei Sun
2020-06-11
1 Introduction
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
2 JHU
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
2.1 time series data
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
2.2 daily reports data
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
3 NY Times
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-06-10"3.1 state level data
First check the 30 states with the largest number of deaths.
## date state fips cases deaths
## 5493 2020-06-10 New York 36 384945 30376
## 5491 2020-06-10 New Jersey 34 165346 12377
## 5482 2020-06-10 Massachusetts 25 104156 7454
## 5474 2020-06-10 Illinois 17 130889 6302
## 5500 2020-06-10 Pennsylvania 42 81410 6143
## 5483 2020-06-10 Michigan 26 65377 5958
## 5464 2020-06-10 California 6 140123 4869
## 5466 2020-06-10 Connecticut 9 44347 4120
## 5479 2020-06-10 Louisiana 22 44143 2968
## 5481 2020-06-10 Maryland 24 60114 2844
## 5469 2020-06-10 Florida 12 67363 2800
## 5497 2020-06-10 Ohio 39 39575 2457
## 5475 2020-06-10 Indiana 18 39297 2355
## 5470 2020-06-10 Georgia 13 51465 2292
## 5506 2020-06-10 Texas 48 81771 1916
## 5465 2020-06-10 Colorado 8 28484 1573
## 5510 2020-06-10 Virginia 51 52177 1514
## 5484 2020-06-10 Minnesota 27 28900 1267
## 5511 2020-06-10 Washington 53 25940 1183
## 5462 2020-06-10 Arizona 4 29981 1100
## 5494 2020-06-10 North Carolina 37 38305 1096
## 5485 2020-06-10 Mississippi 28 18483 868
## 5486 2020-06-10 Missouri 29 15662 861
## 5502 2020-06-10 Rhode Island 44 15756 812
## 5460 2020-06-10 Alabama 1 21989 744
## 5513 2020-06-10 Wisconsin 55 21772 673
## 5476 2020-06-10 Iowa 19 22733 638
## 5503 2020-06-10 South Carolina 45 15759 575
## 5468 2020-06-10 District of Columbia 11 9537 499
## 5478 2020-06-10 Kentucky 21 12029 498For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
3.2 county level data
First check the 50 counties with the largest number of deaths.
## date county state fips cases deaths
## 223426 2020-06-10 New York City New York NA 212884 21436
## 222241 2020-06-10 Cook Illinois 17031 83585 4053
## 221845 2020-06-10 Los Angeles California 6037 67064 2768
## 222933 2020-06-10 Wayne Michigan 26163 21570 2653
## 223425 2020-06-10 Nassau New York 36059 41015 2653
## 223445 2020-06-10 Suffolk New York 36103 40464 1990
## 222845 2020-06-10 Middlesex Massachusetts 25017 22889 1725
## 223351 2020-06-10 Essex New Jersey 34013 18206 1723
## 223346 2020-06-10 Bergen New Jersey 34003 18667 1635
## 223453 2020-06-10 Westchester New York 36119 34075 1530
## 223850 2020-06-10 Philadelphia Pennsylvania 42101 23951 1454
## 221944 2020-06-10 Fairfield Connecticut 9001 16134 1321
## 221945 2020-06-10 Hartford Connecticut 9003 10924 1303
## 223353 2020-06-10 Hudson New Jersey 34017 18647 1242
## 223364 2020-06-10 Union New Jersey 34039 16317 1103
## 223356 2020-06-10 Middlesex New Jersey 34023 16288 1064
## 222914 2020-06-10 Oakland Michigan 26125 11262 1058
## 221948 2020-06-10 New Haven Connecticut 9009 11911 1024
## 222841 2020-06-10 Essex Massachusetts 25009 15365 1024
## 223360 2020-06-10 Passaic New Jersey 34031 16524 982
## 222849 2020-06-10 Suffolk Massachusetts 25025 19099 936
## 222901 2020-06-10 Macomb Michigan 26099 7000 876
## 222847 2020-06-10 Norfolk Massachusetts 25021 8774 873
## 222851 2020-06-10 Worcester Massachusetts 25027 11820 844
## 223359 2020-06-10 Ocean New Jersey 34029 9100 792
## 222000 2020-06-10 Miami-Dade Florida 12086 20276 784
## 223845 2020-06-10 Montgomery Pennsylvania 42091 7709 762
## 222960 2020-06-10 Hennepin Minnesota 27053 9674 693
## 222376 2020-06-10 Marion Indiana 18097 10581 680
## 222827 2020-06-10 Montgomery Maryland 24031 13163 672
## 223822 2020-06-10 Delaware Pennsylvania 42045 6811 662
## 223357 2020-06-10 Monmouth New Jersey 34025 8563 652
## 223871 2020-06-10 Providence Rhode Island 44007 11959 637
## 222843 2020-06-10 Hampden Massachusetts 25013 6395 629
## 223358 2020-06-10 Morris New Jersey 34027 6596 627
## 222828 2020-06-10 Prince George's Maryland 24033 17305 613
## 222848 2020-06-10 Plymouth Massachusetts 25023 8418 608
## 224496 2020-06-10 King Washington 53033 8561 582
## 223411 2020-06-10 Erie New York 36029 6616 563
## 223808 2020-06-10 Bucks Pennsylvania 42017 5340 534
## 221744 2020-06-10 Maricopa Arizona 4013 15282 519
## 222765 2020-06-10 Orleans Louisiana 22071 7279 513
## 223355 2020-06-10 Mercer New Jersey 34021 7245 510
## 221957 2020-06-10 District of Columbia District of Columbia 11001 9537 499
## 222839 2020-06-10 Bristol Massachusetts 25005 7754 487
## 223199 2020-06-10 St. Louis Missouri 29189 5388 485
## 223437 2020-06-10 Rockland New York 36087 13372 466
## 222755 2020-06-10 Jefferson Louisiana 22051 7971 463
## 223362 2020-06-10 Somerset New Jersey 34035 4698 431
## 224385 2020-06-10 Fairfax Virginia 51059 12746 422For these 50 counties, I check the number of new cases and the number of new deaths.
4 COVID Trackng
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they can be much more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
The grades of the states may change over timea and I strongly recommend checking their webiste before puting serious interpretation on the following plot.
5 Session information
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.3.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 lattice_0.20-38 nlme_3.1-144 evaluate_0.14
## [9] lifecycle_0.2.0 tibble_3.0.1 gtable_0.3.0 mgcv_1.8-31
## [13] pkgconfig_2.0.3 rlang_0.4.6 Matrix_1.2-18 yaml_2.2.1
## [17] xfun_0.12 gridExtra_2.3 withr_2.1.2 stringr_1.4.0
## [21] dplyr_0.8.4 knitr_1.28 vctrs_0.3.0 cowplot_1.0.0
## [25] grid_3.6.2 tidyselect_1.0.0 glue_1.3.1 R6_2.4.1
## [29] rmarkdown_2.1 purrr_0.3.3 farver_2.0.3 splines_3.6.2
## [33] scales_1.1.0 ellipsis_0.3.0 htmltools_0.4.0 assertthat_0.2.1
## [37] colorspace_1.4-1 ggsignif_0.6.0 labeling_0.3 stringi_1.4.5
## [41] munsell_0.5.0 crayon_1.3.4